Convective Environments Within Mediterranean Cyclones
Bibliographic record
Abstract
ABSTRACT Mediterranean cyclones can trigger severe weather hazards, including convective precipitation, lightning and hail, with implications for operational forecasting, risk assessment in the insurance industry, and societal preparedness. This work examines the climatological link between Mediterranean cyclones and atmospheric conditions conducive to severe convection. Using ATDnet lightning detections we find that, from autumn to spring, 20%–80% of lightning hours over the Mediterranean basin and adjacent land regions are associated with nearby cyclones. Based on reanalysis data, we demonstrate that severe convective environments and associated hazards predominantly occur in the warm sector of Mediterranean cyclones and to the north‐east of their centres. Convective processes and hazards exhibit a peak approximately 6 h prior to the time of minimum pressure of the cyclone centre, consistent with previous studies. Additionally, we find a strong seasonal dependence of severe convection within cyclones. Severe convective environments are often detected in cyclone types typical of transition seasons (autumn especially) and summer, while they are rarer in deep baroclinic cyclones characteristic of winter. Finally, we provide novel insights regarding the dependence of convective activity on the presence of dynamical features around Mediterranean cyclones. Warm conveyor belts, characterised by large‐scale ascent and high thermodynamic instability, emerge as the most favourable regions for lightning activity, with lightning potential being twice as high compared to cyclone cold fronts. These results advance our understanding of the interplay between cyclone dynamics and severe convection, offering valuable guidance for improving hazard prediction and for elaborating weather emergency strategies in the Mediterranean region.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".